Particle Swarm Optimization Approach for Fuzzy Cognitive Maps Applied to Autism Classification

Oikonomou, Panagiotis; Papageorgiou, Elpiniki I.

doi:10.1007/978-3-642-41142-7_52

Cited by 14 publications

(7 citation statements)

References 18 publications

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“…For example, Alizadeh et al [8] adopted Tabu Search while Ghazanfari et al [31] resorted to the search capabilities of Simulated Annealing and Genetic Algorithms. Other search procedures include: Artificial Inmune Systems [59], Chaotic Simulated Annealing [6], Big Bang -Big Crunch [136], Extended Great Deluge [12], Artificial Bee Colony [135], Particle Swarm Optimization [82], Cultural Algorithm [4], Bacterial Evolutionary Algorithm [16], Structure Optimization Genetic Algorithm [112], Imperialist Competitive Algorithm [3], etc.…”

Section: Error-driven Approachesmentioning

confidence: 99%

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A review on methods and software for fuzzy cognitive maps

et al. 2017

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Fuzzy Cognitive Maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.

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Section: Error-driven Approachesmentioning

confidence: 99%

“…In the supervised learning case, the authors in [82] resorted to a Swarm Intelligence method for predicting autistic disorder in children. The network topology was initially proposed in [45] and comprises 24 concepts: 11 independent input concepts, 12 dependent input concepts and a single output concept using a thresolding approach.…”

Section: Definitionmentioning

confidence: 99%

A review on methods and software for fuzzy cognitive maps

et al. 2017

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“…For instance, in [3] the value of γ has been adopted to 5. Furthermore, a dynamical optimization of γ has been suggested in [33,34] to choose the optimum value according to the problem definition.…”

Section: Fuzzy Cognitive Maps Fundamentalsmentioning

confidence: 99%

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Orang¹,

Silva²,

Guimarães³

2022

Preprint

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Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.

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“…The former are Hebbian-based algorithms [17,44,45], mainly including NHL (nonlinear Hebbian learning) and AHL (active Hebbian learning). The latter are learning algorithms based on evolution theory [17,46,47], which are composed of PSO (particle swarm optimization), RCGA (real coded genetic algorithm), etc. The evolutionary learning can obtain the cause-effect relationships of FCM from the time series data.…”

Section: Proposed Problemsmentioning

confidence: 99%

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

Peng

2017

Sustainability

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Abstract:Haze-fog has seriously hindered the sustainable development of the ecological environment and caused great harm to the physical and mental health of residents in China. Therefore, it is important to probe the formation of haze-fog for its early warning and prevention. The formation of haze-fog is, in fact, a fuzzy nonlinear process. The formation of haze-fog is such a complex process that it is difficult to simulate its dynamic evolution using traditional methods, mainly because of the lack of their consideration of the nonlinear relationships. It is, therefore, essential to explore new perspectives on the formation of haze-fog. In this work, previous research on haze-fog formation is summarized first. Second, a new perspective is proposed on the application of fuzzy cognitive map to the formation of haze-fog. Third, a data mining method based on the genetic algorithm is used to discover the causality values of a fuzzy cognitive map (FCM) for haze-fog formation. Finally, simulation results are obtained through an experiment using the fuzzy cognitive map and its data mining method for the formation of haze-fog. The validity of this approach is determined by definition of a simple rule and the Kappa values. Thus, this research not only provides a new idea using FCM modeling the formation of haze-fog, but also uses an effective method of FCM for solving the nonlinear dynamics of the haze-fog formation.

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Particle Swarm Optimization Approach for Fuzzy Cognitive Maps Applied to Autism Classification

Cited by 14 publications

References 18 publications

A review on methods and software for fuzzy cognitive maps

A review on methods and software for fuzzy cognitive maps

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

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